Challenging the Recognition of Facial Expression via Deep Learning
2014 ◽
Vol 571-572
◽
pp. 717-720
Keyword(s):
A deep Neural Network model was trained to classify the facial expression in unconstrained images, which comprises nine layers, including input layer, convolutional layer, pooling layer, fully connected layers and output layer. In order to optimize the model, rectified linear units for the nonlinear transformation, weights sharing for reducing the complexity, “mean” and “max” pooling for subsample, “dropout” for sparsity are applied in the forward processing. With large amounts of hard training faces, the model was trained via back propagation method with stochastic gradient descent. The results of shows the proposed model achieves excellent performance.
2021 ◽
Vol 37
(1)
◽
pp. 126-136
Keyword(s):
Wireless Brain Wave Classification for Alzheimer’s Patients via Efficient Neural Network Computation
2018 ◽
Vol 10
(03)
◽
pp. 1850004
2018 ◽
Vol 4
(1)
◽
pp. 3
Keyword(s):
Keyword(s):